Abstract
Event-related potentials (ERPs) recorded on the surface of the head are a mixture of signals from many sources in the brain due to volume conductions. As a result, the spatial resolution of the ERPs is quite low. Blind source separation can help to recover source signals from multichannel ERP records. In this study, we present a novel implementation of a method for decomposing multi-channel ERP into components, which is based on the modeling of second-order statistics of ERPs. We also report a new implementation of Bayesian Information Criteria (BIC), which is used to select the optimal number of hidden signals (components) in the original ERPs. We tested these methods using both synthetic datasets and real ERPs data arrays. Testing has shown that the ERP decomposition method can reconstruct the source signals from their mixture with acceptable accuracy even when these signals overlap significantly in time and the presence of noise. The use of BIC allows us to determine the correct number of source signals at the signal-to-noise ratio commonly observed in ERP studies. The proposed approach was compared with conventionally used methods for the analysis of ERPs. It turned out that the use of this new method makes it possible to observe such phenomena that are hidden by other signals in the original ERPs. The proposed method for decomposing a multichannel ERP into components can be useful for studying cognitive processes in laboratory settings, as well as in clinical studies.
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Data Availability
No specific synthetic datasets used in this work were generated. All synthetic datasets were generated just before use and were not saved. The algorithm for generating these data is described in Supplementary materials 1. A C + + implementation of this algorithm is available from the corresponding author on reasonable request. The results of the analysis of real ERP taken from a large data set (HBI database) are given for illustrative purposes only, since the properties of hidden sources of real ERP are not known in advance.
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Acknowledgements
Special thanks to A. Muller (Children’s Research Center, Chur, Switzerland) for organizing EEG recordings in the visual cued Go-Nogo task.
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This work was carried out as part of the State Assignment of the Ministry of Education and Science of the Russian Federation (project 122041300021-4).
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by VAP and JDK. The first draft of the manuscript was written by VAP. The manuscript was edited and revised by VAP and JDK. All authors read and approved the final version of manuscript.
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During the collection of a large data set (HBI database), all studies conducted in accordance with principles for human experimentation as defined in the Declaration of Helsinki and International Conference on Harmonization Good Clinical Practice guidelines, and approved by the Ethics Committee of the N. P. Bechtereva Institute of the Human Brain, Russian Academy of Sciences (IHB RAS), St. Petersburg, Russia and the Local ethics committee of the canton Graubunden, district of Grison (Switzerland).
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Ponomarev, V.A., Kropotov, J.D. Second Order Blind Identification of Event Related Potentials Sources. Brain Topogr 36, 797–815 (2023). https://doi.org/10.1007/s10548-023-00998-1
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DOI: https://doi.org/10.1007/s10548-023-00998-1